[1]乔 通,周 洲,程 鑫,等.基于 Q-学习的底盘测功机自适应 PID 控制模型[J].计算机技术与发展,2022,32(05):117-122.[doi:10. 3969 / j. issn. 1673-629X. 2022. 05. 020]
　QIAO Tong,ZHOU Zhou,CHENG Xin,et al.Adaptive PID Control Model of Chassis Dynamometer Based on Q-Learning[J].,2022,32(05):117-122.[doi:10. 3969 / j. issn. 1673-629X. 2022. 05. 020]
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基于 Q-学习的底盘测功机自适应 PID 控制模型()
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《计算机技术与发展》[ISSN:1006-6977/CN:61-1281/TN]

卷:
32
期数:
2022年05期
页码:
117-122
栏目:
应用前沿与综合
出版日期:
2022-05-10

文章信息/Info

Title:
Adaptive PID Control Model of Chassis Dynamometer Based on Q-Learning
文章编号:
1673-629X(2022)05-0116-06
作者:
乔 通1; 2 ; 周 洲1; 2 ; 程 鑫1; 2 ; 郭兰英1 ; 王润民1; 2
1. 长安大学 信息工程学院,陕西 西安 710064;
2. 陕西省车联网与智能汽车测试技术工程研究中心,陕西 西安 710064
Author(s):
QIAO Tong1; 2 ; ZHOU Zhou1; 2 ; CHENG Xin1; 2 ; GUO Lan-ying1 ; WANG Run-min1; 2
1. School of Information Engineering,Chang’an University,Xi’an 710064,China;
2. Shaanxi Engineering Research Center of Internet of Vehicles and Intelligent Vehicle Testing Technology,Xi’an 710064,China
关键词:
强化学习; PID 控制; Q 学习; 控制策略; 底盘测功机
Keywords:
reinforcement learning; PID control; Q learning; control strategy; chassis dynamometer
分类号:
TP391
DOI:
10. 3969 / j. issn. 1673-629X. 2022. 05. 020
摘要:
为了解决汽车底盘测功机控制系统在动态控制时出现延迟较高和误差大的问题,提出了一种基于强化学习的底盘测功机控制策略。 以 PID 控制算法为基础,扭力偏差为控制器输入,调节电压控制量为输出,选择扭力差变化为智能体奖惩的学习策略,通过 Q 学习算法对 PID 参数进行在线自适应整定;在底盘测功机仿真试验中验证了控制器的调控性能,并与传统 PID 控制以及神经网络 PID 控制的结果进行了对比;实验结果表明,基于 Q 学习的自适应 PID 控制模型较传统PID 算法控制周期缩减至 40. 7% ,相较于神经网络 PID 算法控制周期缩短至 27. 9% 。 相对于传统 PID 控制模型与神经网络 PID 模型,基于 Q 学习的自适应 PID 控制模型输出力上升过程稳定且快速。 提出的基于 Q 学习的自适应 PID 控制模型能够有效提升底盘测功机控制精度,满足其使用的工业要求。
Abstract:
In order to solve the problems of high delay and large error in dynamic control of chassis dynamometer control system,a chassis dynamometer control strategy based on reinforcement learning is proposed. Based on the PID control algorithm,the torque deviation is the input of the controller, the control quantity is the output, and the selection of the torque difference is the learning strategy of the intelligent body reward and the penalty, and the PID parameter is adjusted by the Q learning algorithm. In the simulation test of chassis dynamical machine,the control performance of? the controller is verified, and the results of the traditional PID control and the neural network PID control are compared. The experimental results show that the control cycle of the adaptive PID control model based on Q learning is reduced to 40. 7% compared with the traditional PID algorithm, and the control cycle is shortened by 27. 9% compared with the neural network PID algorithm. Compared with the traditional PID control model and neural network PID model,the process of output force rising of the adaptive PID control model based on Q-Learning is stable and fast. The proposed adaptive PID control model based on Q learning can effectively improve the control accuracy of the chassis dynamometer and meet the industrial requirements of the chassis dynamometer.

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更新日期/Last Update: 2022-05-10